Facilitating the use of MODIS data by modelers: A MODIS simulator and matching dataset for the

evaluation of clouds in climate models

Robert Pincus, U. Colorado/NOAA ESRLSteven E. Platnick, NASA/GSFC

Steven A. Ackerman, U. WisconsinRichard Hemler, NOAA/GFDL

A prototype effort (circa 2004)

Q: Why doesn’t ECMWF use MODIS data for evaluation?

A: “It’s in HDF format” (cloud guy) “I don’t know which of the many datasets to use” (aerosol guy)

Result: we made a year’s worth of monthly netCDF files containing a few MODIS variables. These are used in “climate mode.”

(Forecasting centers have other possible uses for MODIS data, including assimilation and forecast evaluation on daily timescales)

Foreshadowing email: “Other quantities (cloud top pressure, particle size) will be useful if we can provide simple recipes that let the modelers make useful comparisons.”

low top mid high

After Klein and Jakob, MWR, 1999

Relative longitude

Rel

ativ

e la

titud

e

0-10 10-20 20

ISCCP ECMWF

ECMWF (IR cloud top)

0

9

-9

0

9

-9

Relative longitude0-10 10-20 20

Sampling model state to treat fractional cloudiness/masking

Sampling model state to treat fractional cloudiness/masking

MODIS complements ISCCP (i)

The record is much shorter and the sampling is poorer, but...

... different information is available

Cloud properties are separated by phase

Cloud particle size

Many observations come with uncertainty estimates

... biases are different

Cloud top pressure is determined using CO2 slicing above ~700 mb

MODIS is more conservative than ISCCP when determining cloud properties (esp. in Collection 5)

MODIS complements ISCCP (ii)

... averaging is different

ISCCP provides albedo-weighted optical thickness

A custom observational data set for climate model evaluation

We are providing daytime-only estimates of

liquid/ice water path, sorted by phase, with uncertainties

cloud fraction: total, low, mid, high (mask), total, liquid, ice, high, mid, low (retrieval)

cloud top pressure

joint optical thickness/cloud top pressure histogram

as monthly means on 1° grid.

These are standard MODIS products converted to CF-compliant netCDF files.

τ, log 10(τ), re,

Differences with ISCCP are real, complicatedH

ighM

iddleLow

0 10050Cloud fraction (%)

Eq

50 N

50 S

Eq

50 N

50 S

Eq

50 N

50 S

ISCCP, March 2004 MODIS, March 2004

180 E 180 E

ISCCP cloud fraction

5020 80

Cloud fraction (%)

MODIS mask

MODIS retrieval

ISCCP cloud fraction

5020 80

Cloud fraction (%)

MODIS mask

MODIS retrieval

126 18

Optical thickness

MODIS (linear mean)

ISCCP mean

MODIS (logarithmic mean)

126 18

Optical thickness

MODIS (linear mean)

ISCCP mean

MODIS (logarithmic mean)

126 18

Optical thickness

MODIS (linear mean)

ISCCP mean

MODIS (logarithmic mean)

A MODIS simulator for climate models (i)

Accepts sub-column inputs of

Provides subcolumn estimates of

re(l,i)(z), τ(l,i)(z) or q(l,i)(z)

(pseudo-retrieval based on near-IR fluxes) re = F−1(F (re(z)))

(no errors, as ISCCP simulator)

(when > 700 mb, use ISCCP IR)

(can be “undetermined”)

τ =

� sfc

TOA

σc(z)dz

pc = 1/2

� τ=2

TOA

p(z)σc(z)dz

P =

� τ=1

TOA

P (z)σc(z)dz

A MODIS simulator for climate models (ii)

Estimate liquid, ice water path from optical thickness, particle size

Aggregate sub-columns

cloud fractions (total, liquid, ice, high, middle, low) in-cloud linear means of all quantitiesin-cloud logarithmic mean for optical thicknessoptical thickness/cloud top pressure joint histogram

Results from GFDL’s new climate model: cloud fraction...

18 27

18

27

Correlation

0.9

Standard deviation (%)

0.7

Sta

ndard d

evia

tion (

%)

MODIS simulator

ISCCP simulator

MODIS obs (cloud mask)

Model definition

... and optical thickness

Standard deviation

3

3

Sta

ndard d

evia

tion

0.4

0.5

0.3

Corre

lation

MODIS Simulator

ISCCP Simulator

ISCCP Ob.

Getting the word out

The observational datasets will be available from

LAADSWEB (http://laadsweb.nascom.nasa.gov/) and

CF-OBS (http://climserv.ipsl.polytechnique.fr/cfmip-obs.html)

The MODIS simulator is available as part of COSP 1.2

(http://www.cfmip.net/)

We are writing a paper for BAMS describing

the data, the simulator, guidance in interpretation

We designed Level-3 products to be monolithic - something for everybody.

Should we consider targeting specific end-user communities? Which ones?